Method and apparatus for object tracking prior to imminent collision detection

ABSTRACT

A method and apparatus for performing collision detection is described. An object is detected within a first operational range of an object tracker. A classification of the object is determined using the object tracker. The object tracker tracks the object. The object is detected within a second operational range of a collision detector. A safety measure is activated based on the classification using the collision detector.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.11/013,087, filed Dec. 15, 2004, entitled “Method And Apparatus ForObject Tracking Prior To Imminent Collision Detection” which claimsbenefit of U.S. provisional patent application Ser. No. 60/529,481,filed Dec. 15, 2003, all of which applications are herein incorporatedby reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments of the present invention generally relate to vision systems,e.g., as deployed on a vehicle. In particular, this invention relates totracking objects and detecting imminent collisions using stereo vision.

2. Description of the Related Art

Significant interest exists in the automotive industry for systems thatdetect imminent collisions in time to avoid that collision or tomitigate its damage. Collision avoidance systems typically must detectthe presence of potential threats, determine their speed and trajectory,and assess their collision threat. Prior art collision avoidance systemshave used radar to determine the range and closing speed of potentialthreats. However, affordable radar systems usually lack the requiredspatial resolution to reliably and accurately determine the size and thelocation of potential threats.

Since stereo vision can provide the high spatial resolution required toidentify potential threats, stereo vision has been used in collisiondetection and avoidance systems. However, present collision detectingand avoidance systems are not capable of determining a classification ofan object that is in danger of imminent collision. Without knowing theclassification of an object, an avoidance system is not capable ofproviding safety measures that depend on the type of object that is inimminent danger of collision.

Therefore, there is a need in the art for new techniques of using stereovision for collision detection and avoidance.

SUMMARY OF THE INVENTION

The present invention generally relates to a method and apparatus forperforming collision detection. In one embodiment, an object is detectedwithin a first operational range of an object tracker. A classificationof the object is determined using the object tracker. The object trackertracks the object. The object is detected within a second operationalrange of a collision detector. A safety measure is activated based onthe classification using the collision detector.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentinvention can be understood in detail, a more particular description ofthe invention, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 depicts a schematic view of a vehicle utilizing the presentinvention;

FIG. 2 depicts a block diagram of a vehicular vision system inaccordance with the present invention;

FIG. 3 is a depicts a block diagram of functional modules of the visionsystem of FIG. 2; and

FIG. 4 depicts a flow diagram of the operation of a vision system inaccordance with the present invention.

DETAILED DESCRIPTION

The principles of the present invention enhance object tracking andcollision detection systems such that they explicitly communicate targetcharacteristics, such as target type or classification, track targetsover time, use the target classification information at shorteroperational ranges, and take appropriate counter-measure action based ontarget classification to mitigate injury and damage. For example, someembodiments take counter-measures by deploying external airbags withradically different characteristics (such as airbag inflation speed andforce) depending on the target classification (such as a pedestrian, ora vehicle such as a sedan or SUV), and on the time available forcollision mitigation.

The principles of the present invention further provide for a collisiondetection system that detects objects proximate a vehicle. The systemincludes an optical sensor array comprised of stereo cameras thatproduce imagery that is processed to detect pedestrians. Such processingincludes generating a plurality of disparity images at differentresolutions. Those disparity images can be selectively used to producedepth maps (or depth image) of the scene proximate the vehicle byprocessing selected disparity images. The result is depth maps havingdifferent resolutions. The disparity images and/or the depth maps areprocessed and compared to pre-rendered templates of objects. A list ofpossible objects is subsequently produced by matching the pre-renderedtemplates to the disparity images and/or to the depth map. The systemprocesses the possible object list to detect objects near the vehicle.For example, a pedestrian may be detected by eliminating very eccentricpeaks in a correlation image while retaining all peaks with an inverseeccentricity above a predetermined value, e.g. >0.4. Inverseeccentricity is the ratio of the minor and major axes of an ellipsecorresponding to all nearby high correlation scores for the detectedpeaks. This information can be used in a number of ways, e.g., theobjects may be displayed to the driver, a warning may be given, or theinformation may be used in a collision avoidance system that adjusts thetrajectory or other parameters of the vehicle to safely avoid the objector mitigate damage or injury.

FIG. 1 depicts a schematic diagram of a vehicle 100 having a collisionavoidance system 102 that detects one or more objects (e.g., vehicles,pedestrians) 103, 105 within a scene 104 that is proximate the vehicle100. While in the illustrated embodiment the scene 104 is in front ofthe vehicle 100, collision avoidance systems may image scenes that arebehind or to the side of the vehicle 100. The collision avoidance system102 comprises a sensor array 106 that is coupled to an image processor108. The sensors within the sensor array 106 have a field of view thatincludes one or more objects 103, 105.

The field of view in a practical collision avoidance system 102 may be.+−0.6 meters horizontally in front of the vehicle 100 (e.g.,approximately 3 traffic lanes), with a .+−0.3 meter vertical area, andhave a view depth of approximately 12 meters. When the collisionavoidance system 102 is part of a general collision avoidance system,the overall view depth may be 40 meters or so.

FIG. 2 depicts a block diagram of hardware used to implement thecollision avoidance system 102. The sensor array 106 comprises, forexample, a pair of cameras 200 and 202. In some applications, anoptional secondary sensor 204 can be included. The secondary sensor 204may be radar, a LIDAR transceiver, an infrared range finder, a sonarrange finder, and the like. The cameras 200 and 202 generally operate inthe visible wavelengths, but may be augmented with infrared sensors, orthe cameras may themselves operate in the infrared range. The camerashave a known, fixed relation to one another such that they can produce astereo image of the scene 104. Therefore, the cameras 200 and 202 willsometimes be referred to herein as stereo cameras.

Still referring to FIG. 2, the image processor 108 comprises an imagepreprocessor 206, a central processing unit (CPU) 210, support circuits208, and memory 212. The image preprocessor 206 generally comprisescircuitry for capturing, digitizing and processing the imagery from thesensor array 106. The image preprocessor may be a single chip videoprocessor.

The processed images from the image preprocessor 206 are coupled to theCPU 210. The CPU 210 may comprise any one of a number of presentlyavailable high speed microcontrollers or microprocessors. The CPU 210 issupported by support circuits 208 that are generally well known in theart. These circuits include cache, power supplies, clock circuits,input-output circuitry, and the like. The memory 212 is also coupled tothe CPU 210. The memory 212 stores certain software routines that areexecuted by the CPU 210 to facilitate operation of the invention. Thememory also stores certain databases 214 of information that are used bythe invention, and image processing software 216 that is used to processthe imagery from the sensor array 106. The memory may comprise one ormore of random access memory, read only memory, disk drives, opticalstorage, tape storage, removable storage and the like. Although theinvention is described in the context of a series of method steps, themethod may be performed in hardware, software, or some combination ofhardware and software. Additionally, the methods as disclosed can bestored on a computer readable medium such as memory 212.

FIG. 3 is a functional block diagram of modules that are used toimplement the present invention. The stereo cameras 200 and 202 providestereo imagery to a stereo image preprocessor 206. The stereo imagepreprocessor is coupled to a depth map generator 302 which is coupled toan object processor 304. Object processor 304 also comprises objecttracker 308 and collision detector 310. In some applications the depthmap generator 302 is not used. However, the following will describe thefunctional block diagrams under the assumption that a depth mapgenerator 302 is used. The object processor 304 receives informationfrom an object template database 306 and from the optional secondarysensor 204. The stereo image preprocessor 206 calibrates the stereocameras, captures and digitizes imagery, warps the images intoalignment, performs pyramid wavelet decomposition, and performs stereomatching, to create disparity images at different resolutions.

For both hardware and practical reasons, creating disparity imageshaving different resolutions is beneficial when detecting objects suchas pedestrians. Calibration is important as it provides for a referencepoint and direction from which all distances and angles are determined.Each of the disparity images contains the point-wise motion from theleft image to the right image and each corresponds to a different imageresolution. The greater the computed disparity of an imaged object, thecloser the object is to the sensor array.

The depth map generator 302 processes the disparity images intotwo-dimensional depth images. Each depth image (also referred to as adepth map) contains image points or pixels in a two dimensional array,wherein each point represents a specific distance from the referencepoint to a point within the scene 104. The depth images are thenprocessed by the object processor 304 wherein templates (models) ofobjects are compared to the information within the depth image. Inpractice the depth map that is used for comparing with the objecttemplates depends on the distance of the possible pedestrian from thereference point. At a given distance a depth map derived from disparityimages at one resolution have been found superior when template matchingthan another depth map derived from disparity images at anotherresolution. The actually depth map resolution to use in a particularsituation will depend on the particular parameters of the collisionavoidance system 102, such as the type of cameras being used and theircalibration. As described below, the object template database 306comprises templates of objects located at various positions and depthswith respect to the sensor array 106 and its calibration information.

An exhaustive search of the object template database may be performed toidentify an object template that closely matches information in aselected depth map. The secondary sensor 204 may provide additionalinformation regarding the position of an object, e.g., pedestrian 103relative to the vehicle 100 such that the object template search processcan be limited to templates of pedestrians at about the known positionrelative to the vehicle 100. If the secondary sensor 204 is radar, thesecondary sensor can, for example, provide an estimate of bothpedestrian position and distance. Furthermore, the secondary sensor 204can be used to confirm the presence of a pedestrian. The object tracker308 produces an object list that is then used to identify object sizeand classification estimates that enable object tracking of eachpedestrian's position within the scene 104. That object information maythen be used to warn the vehicle 100 driver and or with an automatedsystem to avoid or mitigate damage and injury from object collisions.

FIG. 4 depicts a flow diagram of a method 400 of operating the collisionavoidance system 102. The method begins at step 405 and proceeds to step410 where an object is detected in a first operational range of anobject tracker. Setup and calibration is performed for stereo cameras200, 202. This is typically done only once per configuration.Calibration is used to provide various parameters such as a vehiclereference position, reference camera heights, stereo camera separation,and other reference data for the steps that follow. Images from stereocameras 200, 202 are captured and digitized. The imagery generated fromeach of the cameras is warped into alignment to facilitate producingdisparity images. Warping is performed using the calibration parameters.

A plurality of disparity images from the stereo camera images isgenerated using pyramid wavelet decomposition. Each disparity imagecorresponds to a different image resolution. The disparity images arecreated for each pair of frames generated by the stereo cameras. Thedisparity image comprises, in addition to the disparity information, anindication of which of the disparity pixels in the image are deemedvalid or invalid. Certain disparity values may be deemed invalid becauseof image contrast anomalies, lighting anomalies and other factors. Thisvalid/invalid distinction is used in processing the depth image asdescribed below.

The disparity images are used to produce a depth map. The depth map isproduced using the calibration parameters and a selected disparity mapproduced with a desired resolution. As previously noted, when detectingobjects at a given distance from the vehicle, a depth map derived from adisparity image at one resolution will work better than a depth mapderived from a disparity map having a different resolution. This isbecause of hardware limitations that limit depth map generation andbecause of mathematical conversions when forming depth maps from thedisparity images that produce depth map artifacts that show up as“noise” using one disparity image resolution but not with anotherresolution. The transformation to a depth map is not required. It doeshowever, simplify subsequent computations. The depth map (also known asa depth image or range image) comprises a two-dimensional array ofpixels, where each pixel has a value indicating the depth within theimage at that pixel to a point in the scene from the sensor. As such,pixels belonging to objects in the image will have a depth to the objectand all other pixels will have a depth to the horizon or to the roadwayin front of the vehicle.

To confirm that an object, such as a pedestrian, exists in the field ofview of the stereo cameras, a secondary sensor signal may be used fortarget cueing. This step is optional and may not be required in somesystems. If the secondary sensor 204 is radar, the secondary sensorproduces an estimate of the range and position of an object. The purposeof this optional step is to restrict a subsequent depth map search so asto reduce the search space, and thus reduce the required calculations.Since the pedestrian template search will be restricted to areas at andnear. the radar-provided position and depth estimate, the pedestriantemplate matching process will require less time. This step assists inpreventing false targets by avoiding unnecessary searches.

In step 415, a classification of the object is determined using theobject tracker. An object template database is searched to match objecttemplates to the depth map. The object template database comprises aplurality of pre-rendered object templates, e.g., depth models ofvarious vehicles and pedestrians as they would typically be computed bythe stereo depth map generator 302. The depth image is a two-dimensionaldigital image, where each pixel expresses the depth of a visible pointin the scene 104 with respect to a known reference coordinate system. Assuch, the mapping between pixels and corresponding scene points isknown. In one embodiment, the object template database is populated withmultiple object depth models, one for each object class (e.g. vehicleand pedestrian) for each point in the scene, tessellated in a grid with¼ meters by ¼ meters resolution.

A depth model based search is employed, where the search is defined by aset of possible object location pose pairs. For each such pair, thehypothesized object 3-D model is rendered and compared with the observedscene range image via a similarity metric. This process creates an imagewith dimensionality equal to that of the search space, where each axisrepresents and object model parameter such as but not limited to lateralor longitudinal distance, and each pixel value expresses a relativemeasure of the likelihood that an object exists in the scene 104 withinthe specific parameters. Generally, an exhaustive search is performedwherein an object template database is accessed and the object templatesstored therein are matched to the depth map. However, if the optionaltarget cueing is performed the search space can be restricted to areasat or near objects verified by the secondary sensor. This reduces thecomputational complexity of having to search the complete scene 104.

Matching itself can be performed by determining a difference betweeneach of the pixels in the depth image and each similarly positionedpixels in the object template. If the difference at each pixel is lessthan a predefined amount, the pixel is deemed a match.

A match score is computed and assigned to corresponding pixels within anobject-specific scores image where the value (score) is indicative ofthe probability that the pixel is indicative of the object (e.g., avehicle or a pedestrian). Regions of high density (peaks) in thepedestrian scores image indicate a potential pedestrian 103 in scene104. Regions of high density (peaks) in the vehicle scores imageindicate a potential vehicle 105 in scene 104. Those regions (modes) aredetected using a mean shift algorithm of appropriate scale. Each pixelis shifted to the centroid of its local neighborhood. This process isiterated until convergence for each pixel. All pixels converging to thesame point are presumed to belong to the same mode, and modes thatsatisfy a minimum score and region of support criteria are then used toinitialize the object detection hypotheses.

Object detection includes eliminating certain aspects of a particularobject's scores image or by calculating certain properties of the depthmap. For example, an object may be classified as a pedestrian byeliminating very eccentric peaks in a pedestrian scores image whileretaining all peaks with an inverse eccentricity greater than somepredetermined value, e.g., >0.4. Here, inverse eccentricity is the ratioof the minor and major axes of an ellipse that corresponds to a nearbyhigh correlation score for the detected peak. The effect is to restrictpedestrian detection to objects having a top-down view of a pedestrian,which tend to be somewhat round. An object may be classified as avehicle by computing its width and/or length, depending on what parts ofthe object are visible. For example, if the side of the object isvisible and is measured, in the depth map, to be several meters long,then it is much more likely to be a vehicle than a pedestrian.Conversely, if only the rear of the object is visible, and has a widthof over a meter then it is also much more likely to be a vehicle than apedestrian.

The match scores can be derived in a number of ways. In one embodiment,the depth differences at each pixel between the template and the depthimage are summed across the entire image and normalized by the totalnumber of pixels in the object template. Without loss of generality,these summed depth differences may be inverted or negated to provide ameasure of similarity. Spatial and/or temporal filtering of the matchscore values can be performed to produce new match scores.

As previously noted, the use of a depth map is not required. Forexample, the disparity image produced would not be converted into adepth map. The optional target cueing is performed in the same way theoptional target cueing is performed when using a depth map. However theobject templates are matched against the disparity image produced.Therefore, the object templates are based on disparity images ratherthan on depth maps. A match test is then performed to match the objecttemplates to the multi-resolution disparity image.

In step 420, the object is tracked using object tracker 308. Once anobject has been classified, the object may be further validated. Suchfurther validation ensures that the detected object and itsclassification is more reliable and reduces the possibility of theoccurrence of a false positive. A validation may be performed by stereocamera-based object detection system 102 or an optional secondarysensor, typically radar. If a secondary sensor is utilized, once apossible object is identified, the secondary sensor information iscompared to the identified object to validate that the object 103, 105is truly in the scene 104. In some systems validation by both the stereocamera-based object detection system 102 and by a secondary sensor maybe required. Then, based on the foregoing, an object list is updated andthe objects 103, 105 are tracked. In some systems, objects 103, 105 thatdo not track well can be eliminated as possible objects (being falsepositives). While tracking the objects, the original images from thestereo cameras may be used to identify the boundaries of objects 103,105 within the scene 104. Further, each object is tracked across imageframes such as by using a Kalman filter. Such tracking enables updatingof the classification of the objects 103, 105 using multiple frames ofinformation.

In step 425, an object is detected within an operational range of acollision detector 310. The stereo cameras 200 and 202 provide left andright image stream inputs that are processed to form a stereo depth map.In one embodiment, object tracker 308 and the collision detector 310have the same field of view. Since object tracker 308 and the collisiondetector 310 have the same field of view, a depth map would only need tobe produced once during any given time interval. With the stereo depthdata available, a threat detection and segmentation algorithm detectsobjects in the stereo depth data, and thus in the scene 104 usingcollision detector 310. The threat detection and segmentation stepreturns “bounding boxes” of objects in the stereo depth data.

Once bounding boxes are obtained, the properties of the objects can beobtained from the stereo depth data. The size and height of thepotential threats are determined. The relative position of the potentialthreats are determined. Then a velocity estimation algorithm isperformed that provides velocity estimates for the potential threats.The details of determining those properties are described subsequently.

All of the foregoing properties (e.g., size, height, position, velocity)are estimates that are derived from the stereo depth data, whichincludes image noise. To reduce the impact of that noise, those propertyestimates are time filtered. More specifically, the position andvelocity measurements are filtered using Kalman filters, and a low-passfilter filters noise from the other property estimates. After low passfiltering, the low pass filtered estimates are threshold detected.Threshold detection removes small objects from the potential threatlist.

Once filtered size, position, and velocity estimates are known, thecollision avoidance system 102 performs a trajectory analysis and acollision prediction of the potential threats. That analysis, combinedwith the threshold determination, is used to make a final decision as towhether an imminent collision with a potential threat is likely.

Both object tracker 308 and collision detector 310 use target lists.Target lists are a list of objects, each with its own computedcharacteristics. These computed characteristics may be one or more ofthe following: estimated size, estimated motion vector, range, rangerate, estimated classification, location, size, orientation, edges(e.g., bounding box), estimated crash/collision point, class). Objecttracker 308, if used alone, is useful for classification but does notaccurately obtain collision vector information. Likewise, collisiondetector 310, if used alone, is useful for obtaining collision vectorinformation but does not include reliable classification information.

In order to make a determination in collision detector 310 as to thetype of object that is in danger of imminent collision with vehicle 100,the classification computed in step 415 is communicated to collisiondetector 310. In effect, the object being tracked by object tracker 308is handed off to collision detector 310. This handoff allows for theassociation of a detected object between object tracker 308 andcollision detector 310. The objects may be associated between the twosystems by location, e.g., within a range. The objects may also beassociated by matching additional information such as range rate,closing velocity, velocity vector and collision impact point, height,width and/or length, major-axis orientation, classification, edgesand/or bounding box, image statistics such as image edge density orimage contrast, or depth map statistics such as depth variation or depthmap density.

In one embodiment, the communication of the classification data mayoccur at a pre-selected threshold point where the first operationalrange of object tracker 308 and the second operational range ofcollision detector 310 meet. In the case where there is a hard cut-offfrom object tracker 308 to collision detector 310, extrapolation oftarget list data, e.g., position vector, location vector, velocityvector, is performed.

In one embodiment, the classification may be communicated during anoverlap period (e.g., a period where object tracker 308 and collisiondetector 310 are tracking the same object or objects) of the firstoperational range and the second operational range where there is nopredefined cut-off for the first and second operational ranges. Duringthe overlap period, false positive results may be avoided when in acluttered environment by tracking the object(s) simultaneously usingboth object tracker 308 and collision detector 310.

In one embodiment, a switch from object tracker 308 to collisiondetector 310 is made only when object tracker 308 determines aclassification of an object. In this embodiment, less computationalresources are required since only one system is running at a given time.

There are certain instances where a decision regarding an imminentcollision must be made with very little data, e.g., only one or twoframes of depth map data. In one embodiment, collision detector 310 maybe deployed only when a reliable classification has been made. Inanother embodiment, when there is very little data available, collisiondetector 310 may be deployed whether or not a reliable classificationhas been made.

In step 430, a safety measure based on the classification from step 415is activated using collision detector 310. If a tracked object isdetermined to be in a position that could possibly involve a collisionwith the vehicle 100, object information is provided to the driver andan alarm, or avoidance or damage or injury mitigation mechanism isinitiated. The avoidance or damage or injury mitigation mechanismthresholds are adjusted based on the classification of the object.Finally, the method 400 terminates at step 435.

In practice, some counter-measures used to mitigate damage or injury inan automotive collision may be non-reversible. For example, currentairbag mechanisms trigger only when on-board accelerometers detect thata collision is in progress. This provides only a few tens ofmilliseconds to inflate the airbag, requiring a literally explosive baginflation, which in some cases actually causes injury to the occupant itis intended to protect. Embodiments of the present invention provide forcontrolled counter-measures to mitigate damage and injury upon detectionof an imminent collision. For example, embodiments provide forpre-inflation of an interior airbag without explosive inflation.

Embodiments of the present invention use a wide range of collisionmitigation and/or counter-measures devices. In addition to pre-inflatingan internal and/or external airbag, additional non-reversiblecounter-measures include inflatable external airbags on the front hoodor grill, and actuated mechanical protective devices such as aprotective fence in front of the grill. Alternate counter-measures maybe reversible, such as actuating the host vehicle's brakes and/orreleasing the accelerator, tightening the occupants' seatbelts,actuating a deformable elastic hood to prevent head impacts against theengine block, or even temporarily controlling the host vehicle'ssteering. While these counter-measures may be reversible, their suddenunexpected onset in a non-collision scenario (i.e., a false positive)may also create a shock to the driver such that an accident is caused.Thus while reversible countermeasures may not incur the cost of systemre-arming, the consequences of false triggering may have drasticconsequences and so there is a need for a highly reliable targetdetection and classification systems.

Object tracker 308 and collision detector 310 could operatesimultaneously. However, simultaneous operation of both systems wouldincrease the amount of required computational resources. In oneembodiment, stereo depth map or disparity images are interleaved or timesliced (e.g., the stereo depth map or disparity image frames arealternated) such that object tracker 308 and collision detector 310 areoperating simultaneously but are using less processing resources. Inanother embodiment object tracker 308 and collision detector 310 sharethe same computed stereo depth or disparity map, but each system'sprocessing subsequent to depth map or disparity map generation isinterleaved.

Although the foregoing has described one platform for object tracker 308and collision detector 310 with the same field of view. One having skillin the art could also use two separate platforms for object tracker 308and collision detector 310 having different fields of view. However,using two separate platforms would require more processing resources andwould require more physical resources, which would increase costconsiderably.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

1. A method for performing collision detection, comprising: detecting anobject within a first operational range of an object tracker;determining a classification of said object using said object tracker;tracking said object using said object tracker; detecting said objectwithin a second operational range of a collision detector; andactivating a safety measure based on said classification using saidcollision detector, wherein said safety measure comprises at least oneof a warning, providing object information, an object display and amitigation mechanism.
 2. The method of claim 1 wherein said mitigationmechanism comprises a measure to avoid a damage or injury.
 3. The methodof claim 1 wherein said mitigation mechanism comprises a measure tomitigate a damage or injury.
 4. The method of claim 1 wherein saidmitigation mechanism is reversible.
 5. The method of claim 1 whereinsaid mitigation mechanism is non-reversible.
 6. The method of claim 2further comprising adjusting at least one threshold for said avoidanceor damage or injury mitigation mechanism based on the classification ofthe object.
 7. The method of claim 6 wherein said at least one thresholdcomprises parameters of a vehicle.
 8. The method of claim 1 furthercomprising storing various templates of the objects located at variouspositions and depth with respect to a first sensor.
 9. The method ofclaim 8 wherein said detecting comprises comparing the object templatesto match the object.
 10. The method of claim 8 further comprisingreceiving information regarding the object from a second sensor; andusing the information to limit a number of the stored templates that arecompared to the object.
 11. The method of claim 8 further comprisingreceiving information regarding the object from a second sensor; andusing the information to validate the match between the object and thestored object templates.
 12. The method of claim 1 wherein said detectedobject is within a disparity image of one or more resolutions.
 13. Themethod of claim 1 wherein said detected object is within a depth imageof one or more resolutions.
 14. The method of claim 1 wherein the firstoperational range and the second operational range are mutuallyexclusive.
 15. The method of claim 1 wherein the first operational rangeand the second operational range overlap.
 16. The method of claim 1wherein the object tracker and the collision detector operatesimultaneously.
 17. The method of claim 1 wherein said classification iscommunicated from said object tracker to said collision detector byextrapolating at least one of a positional vector, a location vector,and a velocity vector.
 18. The method of claim 1 further comprisingcommunicating said classification from said object tracker to saidcollision detector.
 19. The method of claim 18 wherein saidclassification is communicated during an overlap period of said firstoperational range and said second operational range.
 20. The method ofclaim 18 wherein said classification is communicated by matchingadditional information from said object tracker and said collisiondetector.
 21. The method of claim 20 wherein said additional informationcomprises at least one of range rate, closing velocity, velocity vectorand collision impact point, height. width and/or length, major-axisorientation, classification, edges and/or bounding box, image statisticssuch as image edge density or image contrast, or depth image statisticssuch as depth variation or depth image density.
 22. The method of claim1 wherein the collision detector becomes operational only when areliable classification is made.
 23. A system for performing collisiondetection, comprising: an object processor having at least an objecttracker and a collision detector, said object processor function todetect an object within a first operational range of the object trackerand within a second operational rage of the collision detector; saidobject tracker function to determine a classification of said object andto track said object; said collision detector function to activate asafety measure based on said classification wherein said safety measurecomprises at least one of a warning, providing object information, anobject display and a mitigation mechanism.
 24. The system of claim 23further comprising a first sensor for sensing said object.
 25. Thesystem of claim 24 wherein said first sensor comprises at least onesensor selected from a group comprising a visible wavelength sensor, aninfrared sensor, a visible wavelength and infrared sensor, a LIDARsensor, a radar sensor, or a SONAR sensor.
 26. The system of claim 24wherein said first sensor comprises a first camera and a second cameraand sensing said object comprises capturing at least one image havingsaid object.
 27. The system of claim 26 wherein said at least one imageis stereo imagery.
 28. The system of claim 27 further comprising animage preprocessor coupled to said first sensor and said objectprocessor, said image preprocessor functions to process the at least oneimage to create at least one disparity image of one or more resolutions.29. The system of claim 28 further comprising a depth image generatorcoupled to said image preprocessor for processing the at least onedisparity image into at least one depth image of one or moreresolutions.
 30. The system of claim 24 further comprising an objecttemplate database coupled to the object processor, said databasefunction to store various templates of the objects located at variouspositions and depth with respect to the first sensor.
 31. The system ofclaim 24 further comprising a second sensor for providing information onthe object, wherein said information comprise a position and a range ofthe object relative to a vehicle.
 32. The system of claim 31 wherein thesecond sensor comprises at least one sensor selected from a groupcomprising a visible wavelength sensor, an infrared sensor, a visiblewavelength and infrared sensor, a LIDAR sensor, a radar sensor, or aSONAR sensor.
 33. The system of claim 24 further comprising a secondsensor for confirming presence of the object.
 34. The system of claim 33wherein the second sensor comprises at least one sensor selected from agroup comprising a visible wavelength sensor, an infrared sensor, avisible wavelength and infrared sensor, a LIDAR sensor, a radar sensor,or a SONAR sensor.
 35. The system of claim 23 wherein said mitigationmechanism comprises a measure to avoid a damage or injury.
 36. Thesystem of claim 23 wherein said mitigation mechanism comprises a measureto mitigate a damage or injury.
 37. The system of claim 23 wherein saidmitigation mechanism is reversible.
 38. The system of claim 23 whereinsaid mitigation mechanism is non-reversible.